Validation of an UV inversion algorithm using satellite and surface measurements
Bibliographic record
Abstract
Ultraviolet radiation in the spectral region between 280 and 315 nm (often referred to as UV‐B) is harmful to living organisms. Satellite‐based estimation of surface UV‐B supplements the sparsely distributed ground‐based UV‐B monitoring networks. This study is concerned with validation of an inversion algorithm [Li et al., this issue] for retrieving spectrally integrated UV‐B (no spectral weighting) and erythemal UV (EUV) (with spectral weighting) fluxes at the surface from satellite. The physical inversion algorithm contains a few analytical expressions and input parameters: the solar zenith angle, ozone amount, albedo at the top of the atmosphere (TOA), and aerosol variables. The algorithm is applied to satellite measurements of total ozone amount and 360 nm reflectance from Meteor 3/TOMS and visible reflectance from NOAA/AVHRR. The retrieved UV‐B and EUV fluxes are compared with ground UV observations made at six Canadian UV observation stations with Brewer instruments from 1992 to 1994. Under all‐sky conditions the comparisons showed very small mean differences and relatively large standard deviations (s.d.): 0.033 W/m2 (mean) and 0.287 W/m2 (s.d.) for total UV‐B and 3.02 mW/m2 (mean) and 12.0 mW/m2 (s.d.) for EUV radiation. The large standard deviations are attributed to the inhomogeneity in sky condition and mobility of cloudy scenes, which renders an inaccurate match between satellite and surface measurements. The comparisons under clear‐sky conditions showed very small mean and standard differences. By means of a running average over a period of time, satellite inversion can track the variation of surface‐observed UV‐B and EUV very well.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".